Course Overview
This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
Who should attend
Data analysts, data engineers, and other data professionals who wish to use Gemini in BigQuery to boost productivity and understand their unstructured data.
Prerequisites
- Prior experience with programming languages including SQL and/or Python.
- Basic knowledge of ML and generative AI.
Course Objectives
- Define the features of Gemini in BigQuery that aid the datato-AI pipeline.
- Explore data with Insights and Table Explorer.
- Develop code with Gemini assistance.
- Discover and visualize workflow with data canvas.
- Explain the workflow of using AI/ML models for predictive and generative tasks in BigQuery.
- Create a solution for leveraging Gemini models in BigQuery with SQL queries and Jupyter Notebooks.
Outline: Gemini in BigQuery for Data Practitioners (GBQDP)
Module 1 - Gemini on BigQuery
Topics:
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[list]
- Gemini on Google Cloud
- Overview of Gemini on BigQuery
- Introduction to course use case
Objectives:
- Understand capabilities of Gemini on Google Cloud.
- Understand capabilities of Gemini on BigQuery.
Module 2 - Data Exploration and Preparation
Topics:
- Data exploration and preparation
- Insights
- Table Explorer
Objectives:
- Discover tools that support data exploration.
- Identify the benefits and restrictions of Insights and Table Explorer.
- Explore data cleaning and pipeline development features in BigQuery.
Activities:
- Lab: Explore Data with Gemini in BigQuery
Module 3 - Code Development with Gemini
Topics:
- Gemini for writing code
- Troubleshooting and testing with Gemini
- Prompting best practices
Objectives:
- Explore using Gemini for writing code.
- Identify how Gemini can assist with troubleshooting.
- Discover prompting best practices.
Activities:
- Lab: Develop Code with Gemini in BigQuery
Module 4 - Data Canvas
Topics:
- Introduction to Data Canvas
- Data Canvas capabilities
- Prompting best practices for Data Canvas
Objectives:
- Explore Data Canvas features.
- Discover prompting best practices for Data Canvas.
Activities:
- Lab: Use Data Canvas to Visualize and Design Queries
Module 5 - Working with Gemini Models in BigQuery
Topics:
- BigQuery ML
- Using Gemini in your SQL queries
- Gemini in BigQuery Notebooks
Objectives:
- Discover the capabilities of BigQuery ML.
- Explore using Gemini in your SQL queries.
- Explore using Gemini in Jupyter Notebooks.
Activities:
- Lab: Analyze Customer Reviews with SQL
- Lab: Analyze Customer Reviews with Python Notebooks